Endothelial Nitric Oxide Synthase Single
Nucleotide Polymorphism and Left
Ventricular Function in Early Chronic Kidney
Disease
Sourabh Chand1,2*, Colin D. Chue3, Nicola C. Edwards3, James Hodson4, Matthew J. Simmonds5, Alexander Hamilton5, Stephen C. L. Gough5,6, Lorraine Harper1,2, Rick P. Steeds3, Jonathan N. Townend3, Charles J. Ferro1, Richard Borrows1,2
1Department of Nephrology, Queen Elizabeth Hospital Birmingham, Birmingham, B15 2WB, United Kingdom,2Centre for Translational Inflammation Research, University of Birmingham, Birmingham, B15 2WB, United Kingdom,3Department of Cardiology, Queen Elizabeth Hospital Birmingham, Birmingham, B15 2WB, United Kingdom,4Department of Statistics, Wolfson Laboratory, Old Queen Elizabeth Hospital, Birmingham, B15 2TH, United Kingdom,5Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford, OX3 7LJ, United Kingdom,6Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LG, United Kingdom
*sourabh.chand@nhs.net
Abstract
Background
Chronic kidney disease (CKD) is associated with accelerated cardiovascular disease and heart failure. Endothelial nitric oxide synthase(eNOS)Glu298Asp single nucleotide poly-morphism (SNP) genotype has been associated with a worse phenotype amongst patients with established heart failure and in patients with progression of their renal disease. The as-sociation of a cardiac functional difference in non-dialysis CKD patients with no known pre-vious heart failure, andeNOSgene variant is investigated.
Methods
140 non-dialysis CKD patients, who had cardiac magnetic resonance (CMR) imaging and tissue doppler echocardiography as part of two clinical trials, were genotyped foreNOS
Glu298Asp SNP retrospectively.
Results
The median estimated glomerular filtration rate (eGFR) was 50mls/min and left ventricular ejection fraction (LVEF) was 74% with no overt diastolic dysfunction in this cohort. There were significant differences in LVEF across eNOS genotypes with GG genotype being associated with a worse LVEF compared to other genotypes (LVEF: GG 71%, TG 76%, TT 73%, p = 0.006). After multivariate analysis, (adjusting for age, eGFR, baseline mean ar-terial pressure, contemporary CMR heart rate, total cholesterol, high sensitive C-reactive OPEN ACCESS
Citation:Chand S, Chue CD, Edwards NC, Hodson J, Simmonds MJ, Hamilton A, et al. (2015) Endothelial Nitric Oxide Synthase Single Nucleotide Polymor-phism and Left Ventricular Function in Early Chronic Kidney Disease. PLoS ONE 10(1): e0116160. doi:10.1371/journal.pone.0116160
Academic Editor:Jaap A. Joles, University Medical Center Utrecht, NETHERLANDS
Received:June 23, 2014
Accepted:December 2, 2014
Published:January 22, 2015
Copyright:© 2015 Chand et al. This is an open ac-cess article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability Statement:All relevant data are within the paper and its Supporting Information files.
Funding:SC is in receipt of Queen Elizabeth Hospi-tal Charity Funding 12-30-46:http://www.qehb.org. MJS is in receipt of a Diabetes, Research and Well-ness Foundation (DRWF) Non-Clinical Fellowship:
https://drwf.org.uk. The funders had no role in study design, data collection and analysis, decision to pub-lish, or preparation of the manuscript
protein, body mass index and gender) GG genotype was associated with a worse LVEF, and increased LV end-diastolic and systolic index (p = 0.004, 0.049 and 0.009
respectively).
Conclusions
eNOSGlu298Asp rs1799983 polymorphism in CKD patients is associated with relevant sub-clinical cardiac remodelling as detected by CMR. This gene variant may therefore rep-resent an important genetic biomarker, and possibly highlight pathways for intervention, in these patients who are at particular risk of worsening cardiac disease as their renal dysfunc-tion progresses.
Introduction
Chronic kidney disease (CKD) is a major public health issue, mainly due to accelerated cardio-vascular disease, affecting an estimated 10–16% of the population in developed countries[1,2]. Non-traditional risk factors and early cardiovascular changes in CKD have been increasingly recognised to lead to heart failure and sudden cardiac death related cardiovascular mortality, implicating left ventricular disease [3,4]. The determinants of the severity of myocardial dis-ease are poorly characterised though hypertension, oxidative stress and activation of the renal angiotensin system are all thought to be relevant. Research into the genetic predisposition to the development of heart failure in CKD has been limited [5].
In the general population, there has been interest in the association between the Glu298Asp polymorphism within endothelial nitric oxide synthase (eNOS) and heart failure [6,7].
Al-though this polymorphism has been associated with endothelial dysfunction and progression of CKD through nitric oxide effects [8], it is not known if this polymorphism is associated with early cardiac structural changes that occur in non-dialysis CKD. In light of this, we investigated if this gene variant is associated with changes in systolic and diastolic function, based on de-tailed cardiac magnetic resonance imaging (CMR) in non-dialysis CKD patients with no known history of heart failure.
Methods
Patients
The study cohort consisted of patients who were initially enrolled into two completed, single centre, randomised controlled trials (CRIB-II [9] and CRIB-PHOS [10]) based at the Queen Elizabeth Hospital Birmingham. To limit the confounding effect of population stratification only white patients (self-reported ethnicity) were included in this genetic substudy (n = 140). Inclusion criteria were: baseline CMR and echocardiographic investigations, age 18–80 with non-dialysis CKD, total cholesterol<5.5mmol/L, resting blood pressure controlled to<140/90
Genotyping
Whole blood (8.5ml) was collected in PAXgene Blood DNA Tubes (Qiagen, Manchester, UK) which were then frozen and stored at -80°C. Samples were defrosted at room temperature for 2 hours before starting the DNA extraction process. DNA extraction was undertaken using the PAXgene Blood DNA Kit (Qiagen Manchester, UK) as previously described [11]. All DNA samples were then all diluted to a working stock of 4ng/ml with 2.25ml of DNA added into each
reaction.eNOS(G894T) SNP rs1799983 genotyping was performed using Taqman technology
as previously described [12]. 384-well plates were read using a 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, USA).
Cardiovascular Magnetic Resonance Imaging
CMR was performed on a 1.5-T scanner (Sonata Symphony, Siemens, Erlangen, Germany). Serial contiguous short axis cines were piloted from the vertical long axis and horizontal long axis of the right and left ventricle (electrocardiogram gated, steady-state free precession imag-ing [True-Fisp]; temporal resolution 40–50ms, repetition time 3.2ms, echo time 1.6ms, flip angle 60°, slice thickness 7mm) in accordance with previously validated methodologies [13]. Analysis was performed off-line (Argus Software, Siemens) by two blinded observers (NE and CDC) for the assessment of ventricular volumes (end-diastole, end-systole, stoke volume) and ejection fraction. Heart rate and baseline brachial blood pressure was measured at the time of CMR.
Echocardiography
Transthoracic echocardiography (Vivid 7; GE Vingmed Ultrasound, Horten, Norway) was per-formed by an experienced echocardiographer. All parameters were measured in triplicate ac-cording to American Society of Echocardiography recommendations [14] and analysed offline (EchoPAC; GE Vingmed Ultrasound, Horten, Norway) by two blinded observers (NE and CDC ). Resting left ventricular (LV) diastolic function was assessed using standard techniques [15].
Arterial Stiffness and Distensibility
Pulse wave velocity, augmentation index and ascending aortic distensibility were common measures of arterial stiffness and distensibility in the two randomised control trials, they have been included here. Pulse wave analysis was performed on the radial artery using a high-fidelity micromanometer (SPC-301; Miller Instruments, Houston, TX). The peripheral arterial wave-form was used to generate a central arterial wavewave-form using a validated transfer function (SphygmoCor; AtCor Medical, Sydney, Australia). The same system was used to determine aortic pulse wave velocity by sequentially recording ECG-gated carotid and femoral waveforms as previously described [16]. CMR data was used for the ascending aortic distensibility mea-surement data.
Outcome Measures
The primary aim was to evaluate the association between LV ejection fraction (LVEF) with
eNOSgenotype. Secondary outcomes included: LV end-diastolic volume indexed to body
to separate the cohort into two risk categories of diastolic dysfunction; these were then assessed byeNOSgenotype. The“diastolic”parameters included: e’lat (early diastolic velocity of the
basal anterolateral LV wall on tissue doppler), E/e’lat (maximum velocity of the E-wave of mi-tral valve inflow by the maximal anterolateral LV wall velocity of e’), mitral valve E/A (ratio of early to late mitral inflow velocities), LVMI (left ventricular mass index), mitral valve propaga-tion velocity (MV VP) and LAVI (left atrial volume index).
Statistical Analysis
Baseline demographics and cardiac investigations were compared across the genotype groups, using Kruskal-Wallis (continuous data) and Fisher’s Exact tests (categorical data) as appropri-ate. The relationship between these was then investigated using regression analysis. Initially, univariate linear regression models were produced, with variables being log-transformed where there was evidence of a non-linearity. Multivariate regression models were used (includ-ing all factors simultaneously) in order to adjust for potentially confound(includ-ing factors.
A cluster analysis was then performed, to divide patients into groups based on the values of a range of diastolic parameters. The Two-Step cluster analysis in IBM SPSS 19 was used, with the number of clusters determined automatically (in this study, two main clusters).
Demographic factors were then compared between the resulting clusters, using t-tests or Fish-er’s Exact test, as applicable. All analyses were performed using IBM SPSS 19 (IBM Corp. Armonk, NY), with p<0.05 deemed to be indicative of statistical significance.
Results
Genomic DNA was successfully genotyped in 132 (>94%) patients. TheeNOSSNP rs1799983
patient genotype frequency was GG in 47% (62/132), TG in 39% (51/132), and TT in 14% (19/132). This distribution was within Hardy-Weinberg equilibrium bounds (p>0.05). Patient
demographics are presented inTable 1for the cohort as a whole and stratified by genotype (GG, TG and TT).There were no significant demographic differences across the three geno-types. Median age was 57 years, eGFR was 50.5 mls/min/1.73m2, with 85% of patients pre-scribed an angiotensin converting enzyme inhibitor or angiotensin II receptor blocker. The most common renal disease group was glomerular disease, with 16% of the cohort patients been diagnosed with IgA nephropathy on renal histology.
Univariate analysis revealed a significant difference across genotypes for LVEF and LVESVI (p = 0.006 and p = 0.024 respectively,Table 2). Post hoc Mann-Whitney U testing demonstrat-ed significant differences between GG genotype and both TG and TT genotypes (p<0.05 for
both). Because of this, and to align with existing literature, further analysis compared the GG group with the non-GG (TG+TT) group, i.e. a“dominant”model. Linear regression analysis revealed an absolute 4% lower LVEF (p = 0.005) and a 21% relative increased LV end-systolic volume index (p = 0.011) in GG versus non-GG (TG+TT) genotyped patients as shown in
Table 3. In multivariate analysis, (adjusting for age, gender, estimated glomerular filtration rate
(eGFR), CMR heart rate (HR), total cholesterol, high sensitive C-reactive protein (hsCRP), body mass index (BMI—if variables were indexed accounting for body surface area, BMI was excluded in the analysis) and brachial mean arterial pressure (BMAP)) the GG genotype and male sex were associated with significantly lower LVEF (p = 0.004 and p = 0.017 respectively;
Table 3). Male gender, GG genotype, eGFR, and heart rate were independently associated with
a higher end systolic and diastolic volume index. A trend for GG genotype association with lower S’lat was seen, but this failed to reach statistical significance (p = 0.106). As shown in
S1 Table, increased aortic stiffness was associated age, male gender and BMAP but not GG
A cluster analysis was performed based on diastolic parameters, in order to group patients based on their risk of diastolic function (Table 4). The most important factor in defining the clusters was found to be e’lat, with mitral valve E/A (MV E/A) and E/e’lat being moderate contributors, and the remaining parameters being minimally discriminative. The first cluster had lower levels of e’lat, MV E/A and MV VP and higher levels of E/e’lat and LAVI, hence represented those patients at most risk of diastolic dysfunction. Comparisons between the clus-ters found that increased age (p<0.001) and reduced eGFR (p = 0.014) were significantly
asso-ciated with the risk of diastolic dysfunction clusters on univariate analysis (Table 5).
Discussion
In this cohort of white patients with non-dialysis dependent CKD, and without heart failure, GG genotype foreNOSSNP rs1799983 was associated with a significant lower LVEF, greater
LVESVI and greater LVEDVI than those found in non-GG genotypes. The burden of myocar-dial disease in CKD suggests the investigation of stratification by genetic risk in this setting to be a worthwhile endeavour, and this study represents the first such attempt with thiseNOS
polymorphism.
Table 1. Baseline Demographics (p value across the three genotype groups).
Characteristic GG genotype TG genotype TT genotype All p value
Number of patients (%) 62 (47) 51 (39) 19 (14) 132
Age (years) 57 (46–63) 59 (47–66) 58 (42–69) 57 (46–65) 0.690
Estimated glomerularfiltration rate (mls/min/1.73m2) 50 (40–61) 51 (38–56) 45 (32–59) 51 (38–59) 0.686
Male (%) 30 (48) 28 (55) 15 (79) 73 (55) 0.063
High sensitive C-reactive protein (mg/l) 1.59 (0.57–5.00) 1.75 (0.91–5.97) 2.79 (0.67–9.87) 1.87 (0.72–5.62) 0.550 Total cholesterol (mmol/l) 4.7 (4.4–5.4) 4.5 (4.0–5.0) 4.8 (3.6–5.9) 4.6 (4.0–5.2) 0.374 Mean arterial pressure (mmHg) 92 (85–101) 90 (85–100) 91 (85–99) 91 (85–100) 0.898 Systolic blood pressure (mmHg) 129 (117–139) 127 (114–135) 124 (111–139) 127 (115–139) 0.539 Diastolic Blood Pressure (mmHg) 72 (66–81) 73 (67–81) 73 (66–79) 73 (66–80) 0.938 Body surface area (m2) 1.87 (1.77–2.02) 1.90 (1.73–2.03) 1.97 (1.92–2.13) 1.92 (1.79–2.03) 0.061
Body mass index (Kg/m2) 27.5 (24.1–31.4) 27.5 (24.2–31.1) 29.2 (25.7–32.2) 27.7 (24.2–31.4) 0.350 Brain natriuretic peptide (ng/L) 86.6 (34.9–176.2) 84.3 (30.9–205.7) 70.0 (37.1–160.9) 84.4 (33.8–193.0) 0.989
Current Smoker (%) 8 (13) 5 (10) 2 (11) 15 (11) 0.930
Previous Smoker (%) 24 (39) 13 (25) 8 (42) 45 (34) 0.250
Diagnoses
Glomerular diseases (%) 25 (40) 18 (35) 11 (58) 54 (40) 0.230
Systemic Diseases (%) 10 (16) 11 (22) 1 (5) 22 (17) 0.296
Tubulointerstitial diseases (%) 7 (11) 9 (18) 0 16 (12) 0.124
Familial nephropathies (%) 9 (15) 7 (14) 1 (5) 17 (13) 0.681
Miscellaneous (%) 11 (18) 6 (12) 6 (32) 23 (17) 0.147
Medication frequency
Angiotensin conversion enzyme inhibitors (%) 35 (56) 29 (57) 14 (74) 78 (60) 0.401
Angiotensin II receptor blockers (%) 20 (32) 15 (29) 4 (21) 39 (30) 0.681
Βblockers (%) 13 (21) 10 (20) 3 (16) 26 (20) 0.956
Calcium channel blockers (%) 13 (21) 16 (31) 5 (26) 34 (26) 0.449
Alpha blockers (%) 7 (11) 6 (12) 2 (11) 15 (11) 0.999
Diuretics (%) 14 (23) 18 (35) 5 (26) 37 (28) 0.311
Statins (%) 28 (45) 20 (39) 9 (47) 57 (43) 0.765
Previous data from the general population suggest this gene variant represents an attractive candidate SNP, and support the findings of the current study. For instance Velloso et al studied a multi-ethnic Brazilian population and demonstrated increased frequency of GG genotype in patients with systolic heart failure compared with healthy controls [7]. Another Brazilian study showed GG genotype was associated with a near 5% reduction in LVEF compared with TT ge-notype patients, findings very similar to those of the current study [18]. Also noteworthy is the higher all-cause mortality associated with the GG genotype in hypertensive patients [19]. An important aspect of the current study is the inclusion of white patients only, in an attempt to reduce confounding by population stratification. Indeed this is highlighted by the study of Velloso et al which did indeed show differences in genotype frequency at this locus between White and Afro-Brazilian individuals [7]. It should be acknowledged, however, that further validation of these findings in diverse populations are required to confirm the robustness of our (and other’s) findings.
The functional change associated with this gene variant also supports the clinical data. This polymorphism results from the nucleotide guanine substituting thiamine at position 894 of exon 7 on chromosome 7, and results in different cleavage of the eNOS enzyme depending on genotype [20]. The GG genotype of the studied SNP is associated with increased eNOS activity and nitric oxide levels [21,22] and experimental overexpression of eNOS (which is present within ventricular myocytes) results in reduced ventricular function [21,23]. This is particular-ly the case in conditions of oxidative stress such as CKD[24], since“uncoupling”of eNOS may lead to generation of superoxide anion radicals that further exacerbate cardiac dysfunction [25].
The influence of genotype on cardiac function and outcome may be context-specific. Of note, McNamara et al suggested a beneficial effect of GG genotype outcome in patients with Table 2. Cardiac investigations relationship to genotype (p value across the three genotype groups).
Cardiac Investigations GG genotype TG genotype TT genotype All p value
Cardiac Magnetic Resonance Imaging
Left ventricular ejection fraction (%) 71 (65–76) 76 (71–80) 73 (66–78) 74 (68–77) 0.006
Left ventricular end-diastolic volume index (mls/m2) 61 (52–70) 57 (51–66) 59 (51–64) 59 (52–66) 0.344 Left ventricular end-systolic volume index (mls/m2) 17 (13–23) 14 (12–17) 16 (12–20) 16 (12–21) 0.024
Left Ventricular Mass Index (g/m2) 54.0 (45.0–67.0) 54.6 (45.0–67.1) 56.0 (47.0–61.9) 54.1 (45.3–65.7) 0.996
Ascending Aortic Distensibility (x10–3mmHg−1) 2.53 (1.24–4.19) 2.15 (1.18–3.31) 2.11 (1.08–3.41) 2.35 (1.24–3.68) 0.692
CMR Cardiac Output (l/min) 5.12 (4.37–6.17) 5.19 (4.64–5.94) 6.38 (4.37–7.57) 5.26 (4.57–6.30) 0.177
CMR Heart Rate (beats/min) 66 (60–77) 64 (58–77) 78 (62–87) 66 (60–78) 0.074
Tissue Doppler Echocardiography
S’lat, cm/s 8 (7–10) 9 (7–10) 9 (7–11) 8 (7–10) 0.688
e’lat, cm/s 9 (7–12) 9 (8–12) 9 (8–11) 9 (8–12) 0.790
E/e’lat 7.17 (5.73–8.96) 6.95 (6.08–8.40) 6.73 (6.13–7.80) 7.00 (6.00–8.56) 0.786 Mitral valve inflow E/A ratio 1.02 (0.87–1.23) 0.95 (0.81–1.20) 0.93 (0.70–1.12) 0.97 (0.81–1.20) 0.273 Mitral valve propagation velocity 50.3 (41.9–66.7) 50.5 (43.6–65.1) 45 (37.7–65.8) 50.0 (41.0–66.4) 0.701 Left Atrial Volume Index (mls/m2) 26.3 (21.8–32.9) 24.5 (20.6–32.3) 27.3 (22.5–30.5) 26.2 (21.6–32.3) 0.760
Pulse wave velocity (m/s) 8.3 (7.3–9.5) 8.3 (7.2–10.0) 9.5 (6.6–10.7) 8.3 (7.2–10.0) 0.808 Augmentation Index (%) 29.0 (22.8–35.1) 27.3 (20.0–35.3) 27.2 (13.3–33.8) 28.3 (20.7–35.0) 0.691
Key: CMR (cardiac magnetic resonance); S’lat (systolic velocity of the basal anterolateral LV wall); e’lat (early diastolic velocity of the basal anterolateral LV wall on tissue doppler); E/e’lat (maximum velocity of the E-wave of mitral valve inflow by the maximal anterolateral LV wall velocity of e’)
Table 3. Univariate and multivariate analysis of systolic function as compared to GG vs non-GG.
Factor Univariate Multivariate
Coefficient (95% CI) Sig. Coefficient (95% CI) Sig. LVEF
Age 0.07 (−0.04, 0.18) 0.228 0.07 (−0.05, 0.19) 0.274
eGFR 0.00 (−0.09, 0.08) 0.915 0.03 (−0.06, 0.11) 0.541
BMAP −0.09 (−0.21, 0.03) 0.158 −0.06 (−0.19, 0.06) 0.318
CMR HR 0.02 (−0.09, 0.12) 0.737 0.00 (−0.12, 0.12) 0.987
Total Cholesterol −0.68 (−2.14, 0.77) 0.354 −0.70 (−2.32, 0.92) 0.394
Log2hsCRP‡ 0.03 (−0.75, 0.81) 0.936 −0.22 (−1.02, 0.58) 0.586
BMI 0.18 (−0.11, 0.47) 0.218 0.13 (−0.18, 0.45) 0.392
Gender (Female) 3.10 (0.30, 5.90) 0.030 3.62 (0.66, 6.58) 0.017
GG (Yes) −3.95 (−6.70,−1.21) 0.005 −4.24 (−7.12,−1.35) 0.004
LVEDVI
Age −0.01 (−0.20, 0.17) 0.878 0.03 (−0.16, 0.21) 0.782
eGFR 0.16 (0.03, 0.30) 0.015 0.14 (0.01, 0.27) 0.033
BMAP −0.14 (−0.34, 0.06) 0.169 −0.18 (−0.38, 0.01) 0.066
CMR HR −0.20 (−0.37,−0.04) 0.018 −0.23 (−0.41,−0.04) 0.017
Total Cholesterol −0.83 (−3.15, 1.49) 0.478 0.65 (−1.84, 3.15) 0.606
Log2hsCRP‡ −1.09 (−2.32, 0.15) 0.084 −1.20 (−2.40, 0.00) 0.050
Gender (Female) −4.68 (−9.17,−0.18) 0.041 −5.02 (−9.56,−0.48) 0.031
GG (Yes) 4.04 (−0.45, 8.53) 0.078 4.46 (0.02, 8.90) 0.049
LVESVI#
Age −0.40% (−1.00%, 0.20%) 0.195 −0.3% (−0.9%, 0.3%) 0.271
eGFR 0.50% (0.10%, 0.90%) 0.021 0.4% (0.0%, 0.8%) 0.076
BMAP −0.20% (−0.80%, 0.50%) 0.614 −0.3% (−0.9%, 0.3%) 0.336
CMR HR −0.53% (−1.07%, 0.02%) 0.059 −0.6% (−1.2%, 0.0%) 0.057
Total Cholesterol 0.3% (−7.0%, 8.2%) 0.936 3.5% (−4.7%, 12.3%) 0.413
Log2hsCRP‡ −1.9% (−5.8%, 2.2%) 0.357 −1.4% (−5.3%, 2.6%) 0.474
Gender (Female) −16.6% (−28.0%,−3.5%) 0.015 −19.4% (−30.6%,−6.4%) 0.005
GG (Yes) 21.0% (4.6%, 40.0%) 0.011 21.9% (5.3%, 41.1%) 0.009
S’Lat
Age −0.03 (−0.07, 0.00) 0.040 −0.04 (−0.08, 0.00) 0.040
eGFR 0.00 (−0.03, 0.02) 0.759 −0.01 (−0.03, 0.02) 0.606
BMAP 0.02 (−0.02, 0.06) 0.294 0.02 (−0.02, 0.06) 0.248
CMR HR −0.01 (−0.04, 0.02) 0.576 −0.02 (−0.06, 0.02) 0.347
Total Cholesterol 0.13 (−0.28, 0.55) 0.529 0.15 (−0.36, 0.65) 0.571
Log2hsCRP‡ 0.00 (−0.22, 0.22) 0.988 0.00 (−0.24, 0.25) 0.992
BMI 0.03 (−0.06, 0.11) 0.543 0.05 (−0.04, 0.15) 0.278
Gender (Female) 0.30 (−0.51, 1.11) 0.465 0.39 (−0.53, 1.30) 0.405
GG (Yes) −0.45 (−1.26, 0.36) 0.270 −0.73 (−1.62, 0.16) 0.106
p-Values from linear regression analysis#Outcome was log2-transformed prior to analysis to normalise the distribution. Quoted coefficients represent the
percentage increase in the outcome for an increase in one of the factors (or for the stated category relative to the reference).
‡hsCRP was log2-transformed, hence the quoted coefficients relate to an increase of one unit in the log (i.e. a two-fold increase)
Key: eGFR (estimated glomerularfiltration rate; BMAP (brachial mean arterial pressure); CMR HR (cardiac magnetic resonance heart rate); hsCRP (high sensitive C-reactive protein; BMI (body mass index)
established, clinically evident heart failure [6]. Whilst at first sight this data conflicts with the current study, and with that of other reports [7,8], it should be noted that 84% of patients dis-played an ejection fraction35% (97.2% of participants were in NYHA stage II or greater, with>50% in stages III or IV). Furthermore there were differences in age and aetiology
(ischaemic versus non-ischaemic) between genotype groups which may have influenced the re-sults as well as variation in the technique used in measuring ejection fraction. Thus, it is cer-tainly possible that thiseNOSSNP influences outcome differentially depending on the stage of
heart failure studied. Although the present study’s exclusion criteria (diabetes mellitus, periph-eral vascular disease, myocardial infarction, known heart failure, valvular heart disease, atrial fibrillation, dialysis-dependence and uncontrolled hypertension) limits the generalizability of its findings, the exclusion criteria does allow removal of these potential external
factors that affect both eNOS activity and left ventricular function, allowing a more
‘pure’analysis ofeNOSpolymorphism association with LVEF in early CKD. Long-term
fol-low-up of the present study population is also desirable to monitor how these patients’LVEFs and heart failure symptoms develop as their CKD progresses, in relation to theireNOS
genotype.
This study benefits from a uniform technique of detailed CMR assessment of cardiac vol-umes and systolic function, and very careful clinical phenotyping. Although no association with“diastolic dysfunction”parameters derived from echocardiography and genotype was evi-dent, the size of the cohort means that such an effect cannot be excluded, and further study in larger cohorts is required.
Table 4. Relative importance of diastolic parameters and their respective values for cluster separation.
Factor Relative Importance Cluster 1 Cluster 2
e’lat 1.00 7.8 (7.4–8.3) 12.7 (11.9–13.5)
Mitral valve inflow E/A ratio 0.73 0.87 (0.82–0.92) 1.27 (1.18–1.37)
E/e’lat 0.62 8.1 (7.6–8.6) 5.7 (5.4–6.1)
Mitral valve velocity propagation 0.37 46.5 (43.3–49.9) 67.6 (58.8–77.7) Left Atrial Volume Index 0.12 27.0 (25.4–28.6) 23.8 (22.4–25.4) Left Ventricular Mass Index 0.00 54.5 (51.1–58.1) 54.1 (50.3–58.2)
Data reported as:“Mean (95% CI)”.
Key: e’lat (early diastolic velocity of the basal anterolateral LV wall on tissue doppler); mitral valve E/A (ratio of early to late mitral inflow velocities); E/e’lat (maximum velocity of the E-wave of mitral valve inflow by the maximal anterolateral LV wall velocity of e’)
doi:10.1371/journal.pone.0116160.t004
Table 5. Univariate analysis of diastolic parameters clusters.
Factor Cluster 1 Cluster 2 p-Value
Age 60.9 (1.1) 43.8 (1.7) <0.001
Estimated Glomerular Filtration Rate 47.3 (1.8) 54.5 (2.3) 0.014
Brachial Mean Arterial Pressure 92.2 (1.4) 91.4 (1.9) 0.700
Gender (Male) 37 (56.1%) 19 (48.7%) 0.545
Genotype (GG) 28 (42.4%) 22 (56.4%) 0.225
Continuous factors are reported as:“Mean (SE)”, with p-values from independent sample t-tests. Dichotomous factors are reported as:“N (%)”, with p-values from Fisher’s Exact Test.
In summary,eNOSGlu298Asp polymorphism in non-dialysis CKD patients is associated
with relevant sub-clinical cardiac remodelling as detected by CMR. This gene variant may therefore represent an important genetic biomarker, and possibly highlight pathways for inter-vention, in these patients who are at particular risk of worsening cardiac disease as their renal dysfunction progresses.
Supporting Information
S1 Table. Univariate and multivariate analysis of arterial stiffness and arterial distensibility as compared to GG vs non-GG.
(DOCX)
Author Contributions
Conceived and designed the experiments: SC CDC NCE JH LH RPS JNT CJF RB. Performed the experiments: SC CDC NCE MJS AH SCLG. Analyzed the data: SC CDC NCE JH MJS SCLG LH RPS JNT CJF RB. Contributed reagents/materials/analysis tools: SC CDC NCE JH MJS AH SCLG LH RPS JNT CJF RB. Wrote the paper: SC CDC NCE JH MJS AH SCLG LH RPS JNT CJF RB.
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